By: Prof. dr Radovan Stojanovic, University of Montenegro PPD · Prof. dr Radovan Stojanovic,...
Transcript of By: Prof. dr Radovan Stojanovic, University of Montenegro PPD · Prof. dr Radovan Stojanovic,...
Geographical Information
Processing for Environmental
Pollution-Related Security within
Urban Scale Environments
GEPSUS
By:
Prof. dr Radovan Stojanovic,
University of Montenegro
PPD
Science for Peace and Security (SPS)
CONTEXT
Instead introduction
Project description
Case studies
Demonstrations
Science for Peace and Security (SPS)
Instead introduction
Disasters triggered by nature or man-caused
destroy lives and livelihoods.
They effect millions of people every year,
reach or poor.
With help of science and technology, we can
reduce human, physical and financial cost of
disasters by understanding the risks
and applying the science and technology in
preventing and mitigation.
Simple, save lives.
Videos: About us, Flooding, Nuclear, Fire
Science for Peace and Security (SPS)
GEPSUS - Goals
General objective: Given above.
Specific objectives:
Integrated system for environmental pollution-related disasters management based on fusion of Geographical Information Processing, Computer Modeling and Simulation, and Creditable Decision Making.
System simulation and testing in real conditions: industrial and traffic air pollution in the Montenegrin capital of Podgorica, with emphasis on air pollution from KAP.
System evaluation, pilot work, dissemination, embedding project into Montenegrin emergency management measures and institutions.
Science for Peace and Security (SPS)
GEPSUS-Partners
-UNIVERSITY OF MONTENEGRO
-GRAPHITECH (ITALY)
-UNIVERSITY OF MARIBOR (SLOVENIJA)
-EMESCO (ISRAEL)
-HERBEY UNIVERSITY (ISRAEL)
- MINISTRY OF DEFENCE OF MONTENEGRO
- SECTOR FOR EMERGENCY
Science for Peace and Security (SPS)
GEPSUS - problem
Target country – Montenegro
Types of urban pollutions Industrial air pollution
Industrial plants
Industrial facilities
Traffic pollution Number of imported old vehicles increasing
55% of vehicles between 11-15 years old
Annual max concentration of hydrocarbons
exceeded by 400 to 2000% in all 21
cities of Montenegro
Fires Every day we face the Risk from the fires
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GEPSUS-at glance
Imagine real incident, release of gases in Podgorica
Policeman
or
fire-fighter
redirect traffic
to safe route
using
his PDA
(iPhone)
Connected to
The Control
Centre
Control
Centre
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Objects of interest
Podgorica, KAP
Pljevlja, TPP
Industrial facilities
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Expressed as sources
- Each source,
different
mathematical
approaches for
modeling.
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Brussels, September 22, 2009
GEPSUS- General approach Databases:
Population
Housing
Businesses
Topography
Geology
Infrastructure
Structural
Vulnerability
Ground Truth: Imagery, Aerial
Photography
Communication Links
Internet, Land Line,
Satellite
3D GIS GeoBrowser
Modeling knowledge: Chemical release
mathematical models for
dispersion and prediction
simulation models
The disaster simulation
Decision-makers
Disaster Prevention/Mitigation plan with detailed measures and reduction strategy
Disorder management knowledge: Operational and tactical know-how,
field experience
Decision-maker
knowledge: Feature extraction and space
optimizing intelligence to support
actors
GEPSUS platform
Science for Peace and Security (SPS)
CETIEcotoxicologicalResearch Centre
Air PollutionSimulation Models
ServersGPUHPC/
REAReal Estate
Administration
User Input
HMZCGHydrometeorological
Service
Curent state &
Prognosis
Data from the Accident Area
Updated geographic
DataEm
iss.
da
ta
GEPSUSCOMPUTINGFACILITY
AutomaticWeatherStations
AutomaticTelemetricStations
TerrainSurveyData
Automatic input data:
- Meteorological
- Topography, strategic
geoinformation
- Emission inventory
User input data:
- Location Latitude, Longitude
- Emission rate
- Wind Speed, direction (autoamatic)
- Type of Emission
- Weather description (automatic)
- Chemical description
- Release parameters description
- Threat zones determination
...
Output:
- Results provided
via mobile
devices
- Provision of
Results to
Control Centres
- Providing the
Information
to Exposed Population
WeatherSimulation
Models
Weather dataReading UK
Washington USA
Population
Rescue Crew
Database
Threat zonesEvacuation plans
Montenegro, Macro flow of data
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Montenegro, Emergency flow of data
Podgorica, 16-17/03/11Brussels,
September 22, 2009
Global flow
(HMZCG, CETI, EC, AZN)
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Many math and ICT models… Math Models in Meteorology
Math Models in Dispersion
Math transformations in GEO-Spatial Data
Math models in Decision Making
ICT to formalize our math and knowledge and
does it usable.
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Example of modeling
Calculation of
sigma_y
sigma_z
and
concentration
Input parameters:
- Stack position and high
- Speed
- Weather conditions
- Emission rate
- Reflection
- Temperature
Single or superposition
Visualisation
wind
profiles
Point sources (Gaussian model)
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Example of modeling
Stability class A Stability class D
- The situation changes by stability classes of
atmosphere
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Realization
Take the data
Calculate models in software like MATLAB
Output threat zones and rescue instructions to Geo-browser
Take the action Example: Using google earth export threat zones as KML format
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EXAMPLES
CASE I Aluminium Plant (KAP Podgorica).
Lat.: 42.389000, Lon.: 19.218797
Scenario SC1 is performed based on
the real data at 09:00h.
Scenario SC2 is performed based on
prognosis for 12:00h.
Scenario SC3. The validation
was performed on the real
data at 12: 00h
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Scenario (KAP)
Parameter SC1 (9:00) SC2 (12:00) SC3 (12:00)
Emission rate Q [g/s] 76.3 76.3 76.3
Height H [m] 37 37 37
Wind welocity [m/s] 2 3.2 2
Wind direction [deg] 320 160 180
z [m] 0 0 0
Stack x [m] 100 100 100
Stack y [m] 200 200 200
x min [m] 1000 1000 1000
y max [m] 400 400 400
W. condition [A-F] 'A' 'A' 'A'
T [C] 28.5 28 28.7 Real time of simulation execution 5.6.2011 9:00 5.6.2011 9:00 5.6.2011 12:00
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Results (KAP)
A B C
Real, prediction based and observation based pollution concentrations
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Results KAP (cont.)
One could observe extreme change in the direction of air pollutant spread which occurred in the period of three hours
Actual wind change was from 320o (A) at 9:00h to 180o (C) at 12h
The predicted wind direction (B) was 160o and missed for 20o
Due to the larger predicted wind speed of meteorological prognosis, one could observe larger expected perimeter in the predicted run (B)
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EXAMPLES
CASE II
Thermo Electric Plant (Pljevlja). Lat.: 43.333494, Lon.: 19.327311.
Scenario SC1 is performed
based on the real data at 09:00h.
Scenario SC2 is performed
based on prognosis for 12:00h.
Scenario SC3. The validation
was performed on the real data
at 12: 00h
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RESULTS (P TPP)
CASE II
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EXAMPLES • In a small industrial park outside Podgorica, a 1810 lit, 1,2m-diameter, vertical tank contains liquid
benzene. During routine control, a security guard discovers that liquid is leaking out of the tank
through a 0.2m circular hole located 0.254m above the bottom of the tank. He also sees that the liquid
is flowing onto a paved area in the industrial park. The guard thinks that the tank has just been filled
that evening. The temperature on scene is 26°C, with the wind from the southwest at 11km per hour
(as measured at a height of 10 meters by a fixed meteorological tower at the site). The sky is more
than half covered by clouds and the humidity is about 75 percent. A thunderstorm is approaching
from the southwest. There is no low-level inversion. There are very few buildings in the industrial
park and a large grassy field is located to the northeast of the industrial park.
The Local Emergency Planning Committee
has requested that on-scene responders
use ERPG-2 concentrations to define
the toxic endpoints in their analysis
of benzene hazards.
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Results
THREAT ZONE:
Model Run: Heavy Gas
Red : 73 meters --(1000 ppm = ERPG-3)
Orange: 248 meters --(150 ppm = ERPG-2)
Yellow: 503 meters --(50 ppm = ERPG-1)
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Conclusion
Overview of the system complexity at the air-pollution emergency control
Important aspect that emerged in the course of our study is the integration of major governmental institutions which have to provide the input data to the system as well as the response by acting on the field
For the test case the Gaussian model of air pollution dispersion was adequate to show the major characteristic of the particular crisis situation
Rapid prototyping with the MATLAB
Application of Google Earth as the initial GIS system
Better process of system development
Results of the simulation models observed in its final form
Decision makers and emergency response teams
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Concluding Comment
To prepare against disasters, one of the most
effective and crucial requirements is information.
This is the core of GEPSUS.
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DEMONSTRATIONS
> CASE I_II, CASE III, AWARDS
Follows